A meta-analysis of the first-night effect in healthy individuals for the full age spectrum

Association Between EEG Microarousal During Nocturnal Sleep and Next-Day Selective Attention in Mild Sleep-Restricted Healthy Undergraduates

Purpose

To explore whether sleep electroencephalogram (EEG) microarousals of different standard durations predict daytime mood and attention performance in healthy individuals after mild sleep restriction.

Participants and Methods

Sixteen (nine female) healthy college students were recruited to examine the correlations between nocturnal EEG microarousals of different standard durations (≥3 s, ≥5 s, ≥7 s, ≥9 s) under mild sleep restriction (1.5 h) and the following morning's subjective alertness, mood, sustained attention, and selective attention task performance.

Results

Results revealed that mild sleep restriction significantly reduced subjective alertness and positive mood, while having no significant effect on negative mood, sustained attention and selective attention performance. The number of microarousals (≥5 s) was negatively associated with positive mood at 6:30. The number of microarousals was significantly and positively correlated with the response time difference value of disengagement component of the selective attention task at around 7:30 (≥5 s and ≥7 s) and 9:00 (≥5 s). The number of microarousals (≥7 s) was significantly and positively correlated with the inaccuracy difference value of orientation component of the selective attention task at around 9:00.

Conclusion

The number of EEG microarousals during sleep in healthy adults with mild sleep restriction was significantly and negatively related to their daytime positive affect while positively associated with the deterioration of disengagement and orientation of selective attention performance, but this link is dependent on the standard duration of microarousals, test time and the type of task.

Studying sleep: towards the identification of hypnogram features that drive expert interpretation

STUDY OBJECTIVES

Hypnograms contain a wealth of information and play an important role in sleep medicine. However, interpretation of the hypnogram is a difficult task and requires domain knowledge and "clinical intuition." This study aimed to uncover which features of the hypnogram drive interpretation by physicians. In other words, make explicit which features physicians implicitly look for in hypnograms.

METHODS

Three sleep experts evaluated up to 612 hypnograms, indicating normal or abnormal sleep structure and suspicion of disorders. ElasticNet and convolutional neural network classification models were trained to predict the collected expert evaluations using hypnogram features and stages as input. The models were evaluated using several measures, including accuracy, Cohen's kappa, Matthew's correlation coefficient, and confusion matrices. Finally, model coefficients and visual analytics techniques were used to interpret the models to associate hypnogram features with expert evaluation.

RESULTS

Agreement between models and experts (Kappa between 0.47 and 0.52) is similar to agreement between experts (Kappa between 0.38 and 0.50). Sleep fragmentation, measured by transitions between sleep stages per hour, and sleep stage distribution were identified as important predictors for expert interpretation.

CONCLUSIONS

By comparing hypnograms not solely on an epoch-by-epoch basis, but also on these more specific features that are relevant for the evaluation of experts, performance assessment of (automatic) sleep-staging and surrogate sleep trackers may be improved. In particular, sleep fragmentation is a feature that deserves more attention as it is often not included in the PSG report, and existing (wearable) sleep trackers have shown relatively poor performance in this aspect.

Slow-wave sleep and REM sleep without atonia predict motor progression in Parkinson's disease

BACKGROUND

Growing evidence supports the potential role of sleep in the motor progression of Parkinson's disease (PD). Slow-wave sleep (SWS) and rapid eye movement (REM) sleep without atonia (RWA) are important sleep parameters. The association between SWS and RWA with PD motor progression and their predictive value have not yet been elucidated.

METHODS

We retro-prospectively analyzed clinical and polysomnographic data of 136 patients with PD. The motor symptoms were assessed using Unified Parkinson's Disease Rating Scale Part III (UPDRS III) at baseline and follow-up to determine its progression. Partial correlation analysis was used to explore the cross-sectional associations between slow-wave energy (SWE), RWA and clinical symptoms. Longitudinal analyses were performed using Cox regression and linear mixed-effects models.

RESULTS

Among 136 PD participants, cross-sectional partial correlation analysis showed SWE decreased with the prolongation of the disease course (P = 0.046), RWA density was positively correlated with Hoehn & Yahr (H-Y) stage (tonic RWA, P < 0.001; phasic RWA, P = 0.002). Cox regression analysis confirmed that low SWE (HR = 1.739, 95% CI = 1.038-2.914; P = 0.036; FDR-P = 0.036) and high tonic RWA (HR = 0.575, 95% CI = 0.343-0.963; P = 0.032; FDR-P = 0.036) were predictors of motor symptom progression. Furthermore, we found that lower SWE predicted faster rate of axial motor progression (P < 0.001; FDR-P < 0.001) while higher tonic RWA density was associated with faster rate of rigidity progression (P = 0.006; FDR-P = 0.024) using linear mixed-effects models.

CONCLUSIONS

These findings suggest that SWS and RWA might represent markers of different motor subtypes progression in PD.

A Protocol for Evaluating Digital Technology for Monitoring Sleep and Circadian Rhythms in Older People and People Living with Dementia in the Community

Sleep and circadian rhythm disturbance are predictors of poor physical and mental health, including dementia. Long-term digital technology-enabled monitoring of sleep and circadian rhythms in the community has great potential for early diagnosis, monitoring of disease progression, and assessing the effectiveness of interventions. Before novel digital technology-based monitoring can be implemented at scale, its performance and acceptability need to be evaluated and compared to gold-standard methodology in relevant populations. Here, we describe our protocol for the evaluation of novel sleep and circadian technology which we have applied in cognitively intact older adults and are currently using in people living with dementia (PLWD). In this protocol, we test a range of technologies simultaneously at home (7-14 days) and subsequently in a clinical research facility in which gold standard methodology for assessing sleep and circadian physiology is implemented. We emphasize the importance of assessing both nocturnal and diurnal sleep (naps), valid markers of circadian physiology, and that evaluation of technology is best achieved in protocols in which sleep is mildly disturbed and in populations that are relevant to the intended use-case. We provide details on the design, implementation, challenges, and advantages of this protocol, along with examples of datasets.

Collagen peptide supplementation before bedtime reduces sleep fragmentation and improves cognitive function in physically active males with sleep complaints

PURPOSE

The primary aim of this study was to examine whether a glycine-rich collagen peptides (CP) supplement could enhance sleep quality in physically active men with self-reported sleep complaints.

METHODS

In a randomized, crossover design, 13 athletic males (age: 24 ± 4 years; training volume; 7 ± 3 h·wk1) with sleep complaints (Athens Insomnia Scale, 9 ± 2) consumed CP (15 g·day1) or a placebo control (CON) 1 h before bedtime for 7 nights. Sleep quality was measured with subjective sleep diaries and actigraphy for 7 nights; polysomnographic sleep and core temperature were recorded on night 7. Cognition, inflammation, and endocrine function were measured on night 7 and the following morning. Subjective sleepiness and fatigue were measured on all 7 nights. The intervention trials were separated by ≥ 7 days and preceded by a 7-night familiarisation trial.

RESULTS

Polysomnography showed less awakenings with CP than CON (21.3 ± 9.7 vs. 29.3 ± 13.8 counts, respectively; P = 0.028). The 7-day average for subjective awakenings were less with CP vs. CON (1.3 ± 1.5 vs. 1.9 ± 0.6 counts, respectively; P = 0.023). The proportion of correct responses on the baseline Stroop cognitive test were higher with CP than CON (1.00 ± 0.00 vs. 0.97 ± 0.05 AU, respectively; P = 0.009) the morning after night 7. There were no trial differences in core temperature, endocrine function, inflammation, subjective sleepiness, fatigue and sleep quality, or other measures of cognitive function or sleep (P > 0.05).

CONCLUSION

CP supplementation did not influence sleep quantity, latency, or efficiency, but reduced awakenings and improved cognitive function in physically active males with sleep complaints.

The first-night effect and the consistency of short sleep in insomnia disorder

The nature and degree of objective sleep impairments in insomnia disorder remain unclear. This issue is complicated further by potential changes in sleep architecture on the first compared with subsequent nights in the laboratory. Evidence regarding differential first-night effects in people with insomnia disorder and controls is mixed. Here, we aimed to further characterize insomnia- and night-related differences in sleep architecture. A comprehensive set of 26 sleep variables was derived from two consecutive nights of polysomnography in 61 age-matched patients with insomnia and 61 good sleeper controls. People with insomnia expressed consistently poorer sleep than controls on several variables during both nights. While poorer sleep during the first night was observed in both groups, there were qualitative differences regarding the specific sleep variables expressing a first-night effect. Short sleep (total sleep time < 6 hr) was more likely during the first night and in insomnia, although approximately 40% of patients with insomnia presenting with short sleep on night 1 no longer met this criterion on night 2, which is important given the notion of short-sleeping insomnia as a robust subtype.

First-night effect reduces the beneficial effects of sleep on visual plasticity and modifies the underlying neurochemical processes

Individuals experience difficulty falling asleep in a new environment, termed the first night effect (FNE). However, the impact of the FNE on sleep-induced brain plasticity remains unclear. Here, using a within-subject design, we found that the FNE significantly reduces visual plasticity during sleep in young adults. Sleep-onset latency (SOL), an indicator of the FNE, was significantly longer during the first sleep session than the second session, confirming the FNE. We assessed performance gains in visual perceptual learning after sleep and increases in the excitatory-to-inhibitory neurotransmitter (E/I) ratio in early visual areas during sleep using magnetic resonance spectroscopy and polysomnography. These parameters were significantly smaller in sleep with the FNE than in sleep without the FNE; however, these parameters were not correlated with SOL. These results suggest that while the neural mechanisms of the FNE and brain plasticity are independent, sleep disturbances temporarily block the neurochemical process fundamental for brain plasticity.

The Effects of Auricular Acupressure on Menopausal Symptoms, Stress, and Sleep in Postmenopausal Middle-Aged Women: A Randomized Single-Blind Sham-Controlled Trial

INTRODUCTION

Middle-aged women in the postmenopausal period experience menopause symptoms, stress, and poor sleep quality due to hormonal changes. Nonetheless, most of them recognize such symptoms as an aging process without receiving proper treatment, and there are few nonpharmacologic interventions available.

METHODS

This was a randomized single-masked, sham-controlled trial. For the intervention group, a vaccaria seed was applied to the auricular points of Shenmen, anterior lobe, adrenal glands, central rim, and endocrine that are related to the menopause symptoms, stress, and sleep while applying a seed to the auricular points not related to the forementioned symptoms to the control group. The Menopause Rating Scale (MRS), Perceived Stress Scale (PSS), heart rate variability, and electroencephalogram (EEG) were measured before the intervention, 4 weeks after the intervention, and 8 weeks after the intervention. Actigraphy was measured with a Fitbit, and the Pittsburgh Sleep Quality Index (PSQI) was measured before and after the intervention. The study was registered with the World Health Organization International Clinical Trials Registry Platform (KCT0007364).

RESULTS

The MRS showed significant differences over time in the intervention group (F, 22.057; P < .001). There was a significant difference over time in the PSS (F, 22.576; P < .001), stress index measured by heart rate variability (F, 14.027; P = .001), and antistress quotient of the right brain measured by EEG (F, 4.865; P = .033). Sleep quality, measured by the PSQI (t = -4.050, P < .001), and sleep efficiency measured by actigraphy (t = 5.996, P < .001) were found to be significantly different over time in the intervention group.

DISCUSSION

This study demonstrated that auricular acupressure is effective in improving menopause symptoms, stress, and sleep in postmenopausal middle-aged women. Therefore, auricular acupressure may be a useful nonpharmacologic intervention for alleviating these symptoms in this population.

Effect of house dust mite allergen on sleep parameters and sleep quality

PURPOSE

The role of nasal problems such as allergic rhinitis in the development of obstructive sleep apnea (OSA) is controversial. The purpose of this study was to analyze the effects of house dust mite (HDM) allergen on sleep-related problems.

METHODS

In a retrospective study patients were classified according to the house dust mite (HDM)-related specific immunoglobulin E (IgE) level into a low HDM-IgE group (group A) and a high HDM-IgE group (group B). Polysomnographic indices, OSA severity, and self-administered questionnaire results were compared between groups. Correlational analysis was used to identify associations between specific IgE values and sleep parameters related to respiratory events.

RESULTS

A total of 327 patients were enrolled. N1 stage ratio, apnea index, and apnea-hypopnea index were significantly higher in group B (P = 0.010, 0.003, and 0.002 respectively) than in group A. N2 stage ratio, and lowest and mean oxygen saturation were significantly lower in group B (P = 0.001, 0.001, and < 0.001 respectively). After propensity score matching, the apnea index and lowest and mean oxygen saturation remained significantly different (P = 0.005, 0.005, and 0.001 respectively). Patients in group B were more likely to have severe OSA and worse subjective sleep quality. In correlational analysis, lowest and mean oxygen saturation were significantly negatively correlated with specific IgE values.

CONCLUSION

A high HDM-specific IgE level was associated with the occurrence of respiratory events and oxygen desaturation during sleep, and with the presence of severe OSA, as well as poorer subjective sleep quality.

Three Contactless Sleep Technologies Compared With Actigraphy and Polysomnography in a Heterogeneous Group of Older Men and Women in a Model of Mild Sleep Disturbance: Sleep Laboratory Study

BACKGROUND

Contactless sleep technologies (CSTs) hold promise for longitudinal, unobtrusive sleep monitoring in the community and at scale. They may be particularly useful in older populations wherein sleep disturbance, which may be indicative of the deterioration of physical and mental health, is highly prevalent. However, few CSTs have been evaluated in older people.

OBJECTIVE

This study evaluated the performance of 3 CSTs compared to polysomnography (PSG) and actigraphy in an older population.

METHODS

Overall, 35 older men and women (age: mean 70.8, SD 4.9 y; women: n=14, 40%), several of whom had comorbidities, including sleep apnea, participated in the study. Sleep was recorded simultaneously using a bedside radar (Somnofy [Vital Things]: n=17), 2 undermattress devices (Withings sleep analyzer [WSA; Withings Inc]: n=35; Emfit-QS [Emfit; Emfit Ltd]: n=17), PSG (n=35), and actigraphy (Actiwatch Spectrum [Philips Respironics]: n=18) during the first night in a 10-hour time-in-bed protocol conducted in a sleep laboratory. The devices were evaluated through performance metrics for summary measures and epoch-by-epoch classification. PSG served as the gold standard.

RESULTS

The protocol induced mild sleep disturbance with a mean sleep efficiency (SEFF) of 70.9% (SD 10.4%; range 52.27%-92.60%). All 3 CSTs overestimated the total sleep time (TST; bias: >90 min) and SEFF (bias: >13%) and underestimated wake after sleep onset (bias: >50 min). Sleep onset latency was accurately detected by the bedside radar (bias: <6 min) but overestimated by the undermattress devices (bias: >16 min). CSTs did not perform as well as actigraphy in estimating the all-night sleep summary measures. In an epoch-by-epoch concordance analysis, the bedside radar performed better in discriminating sleep versus wake (Matthew correlation coefficient [MCC]: mean 0.63, SD 0.12, 95% CI 0.57-0.69) than the undermattress devices (MCC of WSA: mean 0.41, SD 0.15, 95% CI 0.36-0.46; MCC of Emfit: mean 0.35, SD 0.16, 95% CI 0.26-0.43). The accuracy of identifying rapid eye movement and light sleep was poor across all CSTs, whereas deep sleep (ie, slow wave sleep) was predicted with moderate accuracy (MCC: >0.45) by both Somnofy and WSA. The deep sleep duration estimates of Somnofy correlated (r2=0.60; P<.01) with electroencephalography slow wave activity (0.75-4.5 Hz) derived from PSG, whereas for the undermattress devices, this correlation was not significant (WSA: r2=0.0096, P=.58; Emfit: r2=0.11, P=.21).

CONCLUSIONS

These CSTs overestimated the TST, and sleep stage prediction was unsatisfactory in this group of older people in whom SEFF was relatively low. Although it was previously shown that CSTs provide useful information on bed occupancy, which may be useful for particular use cases, the performance of these CSTs with respect to the TST and sleep stage estimation requires improvement before they can serve as an alternative to PSG in estimating most sleep variables in older individuals.

Monitoring sleep stages with a soft electrode array: Comparison against vPSG and home-based detection of REM sleep without atonia

Sleep disorders are symptomatic hallmarks of a variety of medical conditions. Accurately identifying the specific stage in which these disorders occur is particularly important for the correct diagnosis of non-rapid eye movement and rapid eye movement parasomnias. In-lab polysomnography suffers from limited availability and does not reflect habitual sleep conditions, which is especially important in older adults and those with neurodegenerative diseases. We aimed to explore the feasibility and validity of a new wearable system for accurately measuring sleep at home. The system core technology is soft, printed dry electrode arrays and a miniature data acquisition unit with a cloud-based data storage for offline analysis. The positions of the electrodes allow manual scoring following the American Association of Sleep Medicine guidelines. Fifty participants (21 healthy subjects, mean age 56.6 ± 8.4 years; and 29 patients with Parkinson's disease, 65.4 ± 7.6 years) underwent a polysomnography evaluation with parallel recording with the wearable system. Total agreement between the two systems reached Cohen's kappa (k) of 0.688 with agreement in each stage of: wake k = 0.701; N1 = 0.224; N2 = 0.584; N3 = 0.410; and rapid eye movement = 0.723. Moreover, the system reliably detected rapid eye movement sleep without atonia with a sensitivity of 85.7%. Additionally, a comparison between sleep as measured in the sleep lab with data collected from a night at home showed significantly lower wake after sleep onset at home. The results demonstrate that the system is valid, accurate and allows for the exploration of sleep at home. This new system offers an opportunity to help detect sleep disorders on a larger scale than possible today, fostering better care.

Vivid dreams are associated with a high percentage of REM sleep: a prospective study in veterans

STUDY OBJECTIVES

Vivid dreams are dreams that feel real or are associated with dream enactment behavior. They are prevalent in veterans, especially in those with psychiatric disorders such as post-traumatic stress disorders. Such psychiatric disorders have known association with abnormalities in rapid eye movement (REM) sleep. Vivid dreams are also described in neurological conditions, such Lewy body dementias, which are also associated with REM sleep abnormality. Although vivid dreams occur in neuropsychiatric disorders that have REM sleep abnormalities, there are no studies that have directly investigated an association between vivid dreams and REM sleep. We sought to study vivid dreams and REM sleep in veterans.

METHODS

Veterans undergoing polysomnography at our hospital were invited to enroll. Participants completed a dream-related questionnaire the morning after their polysomnography.

RESULTS

We prospectively enrolled 505 veterans. After a night in the sleep laboratory, 196 of 504 (39%) reported experiencing a dream, and, of those, 117 of 190 (62%) described their dream as vivid. Discrepancies in patient totals are secondary to missing questionnaire data. Our novel finding is that participants with a high percentage of REM sleep (above 25%) were more than twice likely to report a vivid dream than participants with a lower percentage of REM sleep (P < .0001). Nonvivid dreams were not associated with a high percentage of REM sleep.

CONCLUSIONS

Vivid dreams are associated with a high percentage of REM sleep. Further research into the role of REM sleep abnormalities in vivid dreams may help to advance understanding of neuropsychiatric disorders.

CITATION

Fattal D, Platti N, Hester S, Wendt L. Vivid dreams are associated with a high percentage of REM sleep: a prospective study in veterans. J Clin Sleep Med. 2023;19(9):1661-1668.

Fascia Ecosystem: A Step Forward in Sleep Engineering and Research

Millions suffer from sleep disorders, and sleep clinics and research institutions seek improved sleep study methods. This paper proposes the Fascia Ecosystem for Sleep Engineering to improve traditional sleep studies. The Fascia Sleep Mask is more comfortable and accessible than overnight stays at a sleep center, and the Fascia Portal and Fascia Hub allow for home-based sleep studies with real-time intervention and data analysis capabilities.A study of 10 sleep experts found that the Fascia Portal is easy to access, navigate, and use, with 44.4% finding it very easy to access, 33.3% very easy to navigate, and 60% very easy to get used to. Most experts found the Fascia Portal reliable and easy to use.Moreover, the study analyzed physiological signals during various states of sleep and wakefulness in two subjects. The results demonstrated that the Fascia dataset captured higher amplitude spindles in N2 sleep (72.20 V and 109.87 V in frontal and parietal regions, respectively) and higher peak-to-peak amplitude slow waves in N3 sleep (93.51 V) compared to benchmark datasets. Fascia produced stronger and more consistent EOG signals during REM sleep, indicating its potential to improve sleep disorder diagnosis and treatment by providing a deeper understanding of sleep patterns.

A new wearable diagnostic home sleep testing platform: comparison with available systems and benefits of multinight assessments

STUDY OBJECTIVES

We assessed the real-world performance of the ANNE Sleep system against 2 Food and Drug Administration-cleared home sleep testing platforms and the intraindividual night-to-night variability of respiratory event index measured by ANNE Sleep.

METHODS

We evaluated the home performance of the ANNE Sleep system compared with 2 Food and Drug Administration-cleared home sleep testing platforms (WatchPAT: n = 29 and Alice NightOne: n = 46) during a synchronous night with unsupervised patient application. Additionally, we evaluated night-to-night variability of respiratory event index and total sleep time using the ANNE Sleep system (n = 30).

RESULTS

For the diagnosis of moderate and severe obstructive sleep apnea, the ANNE Sleep system had a positive percent agreement of 58% (95% confidence interval, 28-85%) and a negative percent agreement of 100% (95% confidence interval, 80-100%) compared to WatchPAT. The positive and negative percent agreement for ANNE Sleep vs Alice NightOne was 85% (95% confidence interval, 66-96%) and 95% (95% confidence interval, 74-100%). There were no differences in mean total sleep time or respiratory event index across multiple nights of monitoring with ANNE. There were no differences consistent with a first-night effect but testing multiple nights reclassified obstructive sleep apnea severity in 5 (17%) individuals and detected 3 additional cases of moderate disease, with only a 12% (standard deviation, 28%) mean fluctuation in respiratory event index from the first night of testing compared to a mean of multiple nights. Overall, 80% of users found ANNE comfortable and easy to use.

CONCLUSIONS

ANNE Sleep exhibited stronger concordance with Alice NightOne compared to WatchPAT. While we illustrated low night-to-night variability for ANNE Sleep, the results suggest multiple nights increased detection of moderate or severe obstructive sleep apnea.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Name: ANNE Diagnostic Agreement With Home Sleep Testing; URL: https://clinicaltrials.gov/ct2/show/NCT05421754; Identifier: NCT05421754.

CITATION

Walter J, Lee JY, Blake S, et al. A new wearable diagnostic home sleep testing platform: comparison with available systems and benefits of multinight assessments. J Clin Sleep Med. 2023;19(5):865-872.

The First-Night Effect on the Instability of Stage N2: Evidence from the Activity of the Central and Autonomic Nervous Systems

A series of studies have suggested that stage N2 is vulnerable and strongly affected by the first-night effect (FNE). However, the neurophysiological mechanism underlying the vulnerability of stage N2 of the FNE has not been well examined. A total of 17 healthy adults (11 women and 6 men, mean age: 21.59 ± 2.12) underwent two nights of polysomnogram recordings in the sleep laboratory. We analyzed sleep structure and central and autonomic nervous system activity during stage N2 and applied the electroencephalographic (EEG) activation index (beta/delta power ratio) and heart rate variability to reflect changes in central and autonomic nervous system activity caused by the FNE. Correlation analyses were performed between EEG activation and heart rate variability. The results showed that EEG activation and high-frequency heart rate variability increased on the adaptation night (Night 1). Importantly, EEG activation was significantly associated with the percentage of stage N1, and the correlation between EEG activation and high-frequency heart rate variability decreased due to the FNE. These findings indicate that the FNE affects the instability of stage N2 by increasing central nervous system activity and uncoupling the activity between the central and autonomic nervous systems.

Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta-analysis of diagnostic test accuracy

There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full-text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea-hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch-by-epoch and event-by-event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta-analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta-analysis will be performed for continuous outcomes using the DerSimonian and Laird random-effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.

Scoring sleep with artificial intelligence enables quantification of sleep stage ambiguity: hypnodensity based on multiple expert scorers and auto-scoring

STUDY OBJECTIVES

To quantify the amount of sleep stage ambiguity across expert scorers and to validate a new auto-scoring platform against sleep staging performed by multiple scorers.

METHODS

We applied a new auto-scoring system to three datasets containing 95 PSGs scored by 6-12 scorers, to compare sleep stage probabilities (hypnodensity; i.e. the probability of each sleep stage being assigned to a given epoch) as the primary output, as well as a single sleep stage per epoch assigned by hierarchical majority rule.

RESULTS

The percentage of epochs with 100% agreement across scorers was 46 ± 9%, 38 ± 10% and 32 ± 9% for the datasets with 6, 9, and 12 scorers, respectively. The mean intra-class correlation coefficient between sleep stage probabilities from auto- and manual-scoring was 0.91, representing excellent reliability. Within each dataset, agreement between auto-scoring and consensus manual-scoring was significantly higher than agreement between manual-scoring and consensus manual-scoring (0.78 vs. 0.69; 0.74 vs. 0.67; and 0.75 vs. 0.67; all p < 0.01).

CONCLUSIONS

Analysis of scoring performed by multiple scorers reveals that sleep stage ambiguity is the rule rather than the exception. Probabilities of the sleep stages determined by artificial intelligence auto-scoring provide an excellent estimate of this ambiguity. Compared to consensus manual-scoring, sleep staging derived from auto-scoring is for each individual PSG noninferior to manual-scoring meaning that auto-scoring output is ready for interpretation without the need for manual adjustment.

Obstructive sleep apnea may reduce a diagnostic accuracy of video-polysomnography for idiopathic REM sleep behavior disorder

BACKGROUND

Severe obstructive sleep apnea (OSA) in patients with rapid eye movement (REM) sleep behavior disorder (RBD) may result in frequent fragmentation of REM sleep and consequently lead to a false negative diagnosis of RBD on a video-polysomnography (video-PSG). Thus, we determined whether OSA has the negative impact on video-PSG diagnostic accuracy for RBD.

METHODS

Patients with clinically diagnosed idiopathic RBD were included. RBD was confirmed if a video-PSG demonstrated complex motor behavior during REM sleep or REM sleep without atonia (RWA). Motor behavior was measured using the RBD Severity Scale. Cohen's kappa coefficient was calculated for qualitative assessment of RWA. A stepwise logistic regression analysis was performed.

RESULTS

Of a total 254 patients included, a diagnosis of RBD was confirmed by a video-PSG in 221 patients (87.0%). RWA, vocalization, and axial or proximal muscle movements in REM sleep were noted in 86.6%, 58.3%, and 35.9%, respectively. A video-PSG diagnosis of RBD was less likely associated with severe OSA (odds ratio [OR] 0.284, p = 0.010) and moderate OSA (OR 0.404, p = 0.071) whereas was more likely associated with longer REM sleep time (OR 1.036, p < 0.001). In 43 patients who underwent a continuous positive airway pressure (CPAP) titration study, a diagnosis of RBD was more common on a CPAP titration study (88.4%) than on a first video-PSG (65.1%) (p = 0.013). Twelve (27.9%) of 43 CPAP patients were diagnosed with RBD according only to CPAP titration study.

CONCLUSIONS

OSA that requires CPAP may reduce a diagnostic accuracy for RBD by a video-PSG. False negative results were probably due to frequent electromyographic artifacts and/or severe sleep disruption from apneic events during REM sleep.

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

Objectives

Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features.

Methods

We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening.

Results

The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk.

Conclusions

The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.

Can we put the first night effect to bed? An analysis based on a large sample of healthy adults

The 'first night effect' refers to individuals experiencing poorer sleep during their first night in a laboratory. The effect is attributed to sleeping in a new environment, as well as wearing electrodes on the head and face, and is often cited as a reason for including an adaptation night in sleep research protocols. However, in the time since the 'first night effect' was initially reported, the conditions and equipment used in modern sleep laboratories have changed considerably, which may reduce the 'first night effect.' The aim of this study was to examine the impact of the 'first night effect' on sleep in a sample of healthy adults. Participants (n = 124; 22.7 ± 3.6 years) were given a 9-hour sleep opportunity (23:00-08:00 h) on two consecutive nights in a time-isolated sleep laboratory with sleep measured via polysomnography. Differences in dependent sleep variables between Night 1 and Night 2 were examined using paired t-tests. There was no difference in sleep onset latency (p = .295), total sleep time (p = .343), wake after sleep onset (p = .410), or sleep efficiency (p = .342) between Nights 1 and 2. However, participants spent more time in stage one (p = .001), and less time in stages two (p = .029) and three (p = .013) on Night 1 compared with Night 2. This suggests that, where primary sleep variables are the focus and not sleep architecture or arousals (e.g., where sleep is used as an independent variable), including an adaptation night may not be necessary.

Associations between risk of Alzheimer's disease and obstructive sleep apnea, intermittent hypoxia, and arousal responses: A pilot study

OBJECTIVES

Obstructive sleep apnea (OSA) may increase the risk of Alzheimer's disease (AD). However, potential associations among sleep-disordered breathing, hypoxia, and OSA-induced arousal responses should be investigated. This study determined differences in sleep parameters and investigated the relationship between such parameters and the risk of AD.

METHODS

Patients with suspected OSA were recruited and underwent in-lab polysomnography (PSG). Subsequently, blood samples were collected from participants. Patients' plasma levels of total tau (T-Tau) and amyloid beta-peptide 42 (Aβ42) were measured using an ultrasensitive immunomagnetic reduction assay. Next, the participants were categorized into low- and high-risk groups on the basis of the computed product (Aβ42 × T-Tau, the cutoff for AD risk). PSG parameters were analyzed and compared.

RESULTS

We included 36 patients in this study, of whom 18 and 18 were assigned to the low- and high-risk groups, respectively. The average apnea-hypopnea index (AHI), apnea, hypopnea index [during rapid eye movement (REM) and non-REM (NREM) sleep], and oxygen desaturation index (≥3%, ODI-3%) values of the high-risk group were significantly higher than those of the low-risk group. Similarly, the mean arousal index and respiratory arousal index (R-ArI) of the high-risk group were significantly higher than those of the low-risk group. Sleep-disordered breathing indices, oxygen desaturation, and arousal responses were significantly associated with an increased risk of AD. Positive associations were observed among the AHI, ODI-3%, R-ArI, and computed product.

CONCLUSIONS

Recurrent sleep-disordered breathing, intermittent hypoxia, and arousal responses, including those occurring during the NREM stage, were associated with AD risk. However, a longitudinal study should be conducted to investigate the causal relationships among these factors.

Impact of Sleep Fragmentation on Cognition and Fatigue

Sleep continuity and efficacy are essential for optimal cognitive functions. How sleep fragmentation (SF) impairs cognitive functioning, and especially cognitive fatigue (CF), remains elusive. We investigated the impact of induced SF on CF through the TloadDback task, measuring interindividual variability in working memory capacity. Sixteen participants underwent an adaptation polysomnography night and three consecutive nights, once in a SF condition induced by non-awakening auditory stimulations, once under restorative sleep (RS) condition, counterbalanced within-subject. In both conditions, participants were administered memory, vigilance, inhibition and verbal fluency testing, and for CF the TloadDback, as well as sleep questionnaires and fatigue and sleepiness visual analog scales were administered. Subjective fatigue increased and sleep architecture was altered after SF (reduced sleep efficiency, percentage of N3 and REM, number of NREM and REM phases) despite similar total sleep time. At the behavioral level, only inhibition deteriorated after SF, and CF similarly evolved in RS and SF conditions. In line with prior research, we show that SF disrupts sleep architecture and exerts a deleterious impact on subjective fatigue and inhibition. However, young healthy participants appear able to compensate for CF induced by three consecutive SF nights. Further studies should investigate SF effects in extended and/or pathological disruption settings.

Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features

Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.

A meta-review of standard polysomnography parameters in Rett Syndrome

Rett Syndrome (RTT, OMIM 312750), a unique rare neurodevelopmental disorder, mostly affects females and causes severe multi-disabilities including poor sleep. This meta-analysis systematically reviewed the polysomnographic (PSG) data of individuals with RTT on both sleep macrostructure and sleep respiratory indexes and compared them to literature normative values. Studies were collected from PubMed, Web of Science, PsycINFO, Ebsco, Scopus, and Cochrane Library till 26 April 2022. Across 13 included studies, the 134 selected RTT cases were mostly females being MECP2 (n = 41) and CDKL5 (n = 4) positive. They were further stratified by gene, age, and clinical features. Findings of comparison with literature normative values suggested shorter total sleep time (TST) and sleep onset latency (SOL), twice as long wake after sleep onset (WASO) with lower sleep efficiency (SEI) in RTT, as well as increased non-rapid eye movement stage 3 (stage N3) and decreased rapid eye movement sleep. Based on limited data per stratifications, we found in RTT cases <5 years old lower stage N3, and in RTT cases >5 years old less WASO and more WASO in the epileptic strata. However, meta-results generated from studies designed with comparison groups only showed lower stage N1 in RTT than in healthy comparison, together with similar SEI and stage N3 to primary snoring subjects. For sleep respiratory indexes, severe disordered sleep breathing was confirmed across roughly all RTT strata. We are the first study to meta-analyze PSG data of subjects with RTT, illustrating shorter TST and aberrant sleep staging in RTT that may vary with age or the presence of epilepsy. Severe nocturnal hypoxemia with apneic events was also demonstrated. More studies are needed to explore and elucidate the pathophysiological mechanisms of these sleep findings in the future.

The Impact of Sleep Inertia on Physical, Cognitive, and Subjective Performance Following a 1- or 2-Hour Afternoon Nap in Semiprofessional Athletes

PURPOSE

This study examined the impact of sleep inertia on physical, cognitive, and subjective performance immediately after a 1- or 2-hour afternoon nap opportunity.

METHODS

Twelve well-trained male athletes completed 3 conditions in a randomized, counterbalanced order-9 hours in bed overnight without a nap opportunity the next day (9 + 0), 8 hours in bed overnight with a 1-hour nap opportunity the next day (8 + 1), and 7 hours in bed overnight with a 2-hour nap opportunity the next day (7 + 2). Nap opportunities ended at 4:00 PM. Sleep was assessed using polysomnography. Following each condition, participants completed four 30-minute test batteries beginning at 4:15, 4:45, 5:15, and 5:45 PM. Test batteries included a warm-up, self-ratings of readiness to perform, motivation to perform and expected performance, two 10-m sprints, 2 agility tests, a 90-second response-time task, and 5 minutes of seated rest.

RESULTS

Total sleep time was not different between conditions (P = .920). There was an effect of condition on readiness (P < .001), motivation (P = .001), and expected performance (P = .004)-all 3 were lower in the 8 + 1 and 7 + 2 conditions compared with the 9 + 0 condition. There was no effect of condition on response time (P = .958), sprint time (P = .204), or agility (P = .240), but a large effect size was observed for agility.

CONCLUSIONS

After waking from a nap opportunity, agility may be reduced, and athletes may feel sleepy and not ready or motivated to perform. Athletes should schedule sufficient time (∼1 h) after waking from a nap opportunity to avoid the effects of sleep inertia on performance.

Association Between Pre-Stroke Subjective Sleep Status and Post-Stroke Cognitive Impairment: A Nationwide Multi-Center Prospective Registry

BACKGROUND

Although sleep disorders significantly increase the risk of cognitive impairment, literature is relatively scarce regarding the impact of sleep status on cognitive function in patients with acute ischemic stroke (AIS). We seek to study the association between pre-stroke subjective sleep status and cognitive function at 3 months after stroke.

PATIENTS AND METHODS

Data were analyzed for 1,759 AIS patients from the Impairment of Cognition and Sleep after Acute Ischemic Stroke or Transient Ischemic Attack in Chinese Patients Study (ICONS). Pre-stroke subjective sleep status was assessed by the Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS). Greater sleep fragmentation was defined as waking up in the middle of the night or early morning ≥3 times a week. Cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA) at 3 months after stroke. Primary endpoint was the incidence of post-stroke cognitive impairment (PSCI) at 3 months after stroke. The association between subjective sleep status and PSCI was evaluated using multivariable logistic regression.

RESULTS

PSCI occurred in 52.1% at 3 months after stroke. Patients with very bad sleep quality before stroke were at increased risk of PSCI (OR, 2.11; 95% CI, 1.11-4.03; P=0.03). Subgroup analysis found that the association between very bad sleep quality and PSCI was more evident among patients with high school education or above (OR, 5.73; 95% CI, 1.92-17.10; P for interaction=0.02). In addition, patients with greater sleep fragmentation before stroke were also at higher risk of PSCI (OR, 1.55; 95% CI, 1.20-2.01; P<0.01). Similarly, subgroup analysis showed that the risk of PSCI was more pronounced among patients without employment (OR, 2.45; 95% CI, 1.59-3.77; P for interaction=0.01).

CONCLUSION

Very bad sleep quality and greater sleep fragmentation before stroke were identified as independent risk factors for PSCI at 3 months after stroke.